1 Learning objectives

  1. You will use dplyr::filter() to keep or drop rows from a dataframe.

  2. You will filter rows by specifying conditions on numbers or strings using relational operators like greater than (>), less than (<), equal to (==), and not equal to (!=).

  3. You will filter rows by combining conditions using logical operators like the ampersand (&) and the vertical bar (|).

  4. You will filter rows by negating conditions using the exclamation mark (!) logical operator.

  5. You will filter rows with missing values using the is.na() function.

2 The Yaounde COVID-19 dataset

In this lesson, we will again use the data from the COVID-19 serological survey conducted in Yaounde, Cameroon.

yaounde <- read_csv(here::here('ch04_data_wrangling/data/yaounde_data.csv'))
## a smaller subset of variables
yao <- yaounde %>% 
  select(age, sex, weight_kg, highest_education, neighborhood, 
         occupation, is_smoker, is_pregnant, 
         igg_result, igm_result)
yao

See the previous lesson for more information about this data.

3 Introducing filter()

We use filter() to keep rows that satisfy a set of conditions. Let’s take a look at a simple example. If we want to keep just the male records, we run:

yao %>% filter(sex == "Male")

Note the use of the double equal sign == rather than the single equal sign =. The == sign tests for equality, as demonstrated below:

## create the object `sex_vector` with three elements
sex_vector <- c("Male", "Female", "Female")
## test which elements are equal to "Male"
sex_vector == "Male"
## [1]  TRUE FALSE FALSE

So the code yao %>% filter(sex == "Male") will keep all rows where the equality test sex == "Male" evaluates to TRUE.

It is often useful to chain filter() with nrow() to get the number of rows fulfilling a condition.

## how many respondents were male?
yao %>% 
  filter(sex == "Male") %>% 
  nrow()
## [1] 422

The double equal sign, ==, tests for equality, while the single equals sign, =, is used for specifying values to arguments inside functions.

Filter the yao data frame to respondents who were pregnant during the survey. Store in q1.

How many respondents were female? (Use filter() and nrow())

4 Relational operators

The == operator introduced above is an example of a “relational” operator, as it tests the relation between two values. Here is a list of some of these operators:

Operator is TRUE if
A < B A is less than B
A <= B A is less than or equal to B
A > B A is greater than B
A >= B A is greater than or equal to B
A == B A is equal to B
A != B A is not equal to B
A %in% B A is an element of B

Let’s see how to use these within filter():

yao %>% filter(sex != "Male") ## keep rows where `sex` is not "Male"
yao %>% filter(age < 6) ## keep respondents under 6
yao %>% filter(age >= 70) ## keep respondents aged at least 70
## keep respondents whose highest education is "Primary" or "Secondary"
yao %>% filter(highest_education %in% c("Primary", "Secondary"))

From yao, keep only respondents who were children (under 18).

With %in%, keep only respondents who live in the “Tsinga” or “Messa” neighborhoods.

5 Combining conditions with & and |

We can pass multiple conditions to a single filter() statement separated by commas:

## keep respondents who are pregnant and are ex-smokers
yao %>% filter(is_pregnant == "Yes", is_smoker == "Ex-smoker") ## only one row

When multiple conditions are separated by a comma, they are implicitly combined with an and (&).

It is best to replace the comma with & to make this more explicit.

## same result as before, but `&` is more explicit
yao %>% filter(is_pregnant == "Yes" & is_smoker == "Ex-smoker")

If we want to combine conditions with an or, we use the vertical bar symbol, |.

## respondents who are pregnant OR who are ex-smokers
yao %>% filter(is_pregnant == "Yes" | is_smoker == "Ex-smoker")

Filter yao to only keep men who tested IgG positive.

Filter yao to include just children (under 18s) and those whose highest education is primary school.

6 Negating conditions with !

To negate conditions, we wrap them in !().

Below, we drop respondents who are children (less than 18 years) or who weigh less than 30kg:

## drop respondents < 18 years OR < 30 kg
yao %>% filter(!(age < 18 | weight_kg < 30))

The ! operator is also used to negate %in% since R does not have an operator for NOT in.

## drop respondents whose highest education is NOT "Primary" or "Secondary"
yao %>% filter(!(highest_education %in% c("Primary", "Secondary")))

It is easier to read filter() statements as keep statements, to avoid confusion over whether we are filtering in or filtering out!

So the code below would read: “keep respondents who are under 18 or who weigh less than 30kg”.

yao %>% filter(age < 18 | weight_kg < 30)

And when we wrap conditions in !(), we can then read filter() statements as drop statements.

So the code below would read: “drop respondents who are under 18 or who weigh less than 30kg”.

yao %>% filter(!(age < 18 | weight_kg < 30))

Drop respondents who live outside of the Tsinga or Messa neighborhoods.

7 NA values

The relational operators introduced so far do not work with NA.

Let’s make a data subset to illustrate this.

yao_mini <- yao %>% 
  select(sex, is_pregnant) %>% 
  slice(1,11,50,2) ## custom row order

yao_mini

In yao_mini, the last respondent has an NA for the is_pregnant column, because he is male.

Trying to select this row using == NA will not work.

yao_mini %>% filter(is_pregnant == NA) ## does not work
yao_mini %>% filter(is_pregnant == "NA") ## does not work

This is because NA is a non-existent value. So R cannot evaluate whether it is “equal to” or “not equal to” anything.

The special function is.na() is therefore necessary:

## keep rows where `is_pregnant` is NA
yao_mini %>% filter(is.na(is_pregnant))

This function can be negated with !:

## drop rows where `is_pregnant` is NA
yao_mini %>% filter(!is.na(is_pregnant))

For tibbles, RStudio will highlight NA values bright red to distinguish them from other values:

Keep all the responders who had missing records for the report of their smoking status

7.1 A common error with NA

Handling NAs improperly is often a source of error. Imagine, for example, that we we wanted to drop pregnant women from the dataset.

We might write filter(is_pregnant != "Yes") (to be read as “keep respondents who are not pregnant”), but this would be wrong!

## keep rows where `is_pregnant` is not "Yes"
yao_mini %>% filter(is_pregnant != "Yes") ## bad

Do you see what went wrong? We wanted to drop the pregnant woman, but we dropped the man too!

This is because filter() drops all rows where the test evaluates to NA. And the test NA != "Yes" evaluates to NA, because R does not not whether NA is equal to or not equal to “Yes”.

NA != "Yes" ## R does not know whether `NA` is equal to or not equal to "Yes"
## [1] NA

In order to correctly filter on a column that contains NAs, we often need to include an is.na() condition. So to drop pregnant women without accidentally dropping the man, we could write:

## keep rows where `is_pregnant` is not "Yes"
yao_mini %>% filter(is_pregnant != "Yes" |
                    is.na(is_pregnant)) ## OR `is_pregnant` is NA

Alternatively, we could write:

## keep rows where `is_pregnant` is `NA`, "No" or "No response"
yao_mini %>% 
  filter(is.na(is_pregnant) | is_pregnant == "No" | is_pregnant == "No response")

A common error with NA{width=“300”,height=“1000”}

For some respondents the respiration rate, in breaths per minute, was recorded in the respiration_frequency column. From yaounde, drop those with a respiration frequency under 20.

8 Other common filters

8.1 row_number()

We sometimes need to filter based on row numbers. The dplyr helper row_number() can help achieve this.

To keep rows 7 to 10 and row 70 we write:

yao %>% filter(row_number() %in% c(7:10, 70))

To drop rows 7 to 10 we write:

yao %>% filter(!row_number() %in% 7:10)

From yao, keep rows 8 to 20 and row 80.

8.2 stringr::str_detect()

The yaounde dataset has a number of multiple response variables.

The occupation variable, for example, lists multiple responses separated by “--”:

## the fifth respondent is a trader and a farmer
yao %>% select(occupation)

Such variables require special handling.

If we want to subset the data to farmers, we should not write filter(occupation == "Farmer"), as that would drop anyone who has a second or third occupation (e.g. “Trader–Farmer”).

The str_detect() function is useful here. This function checks a given string (first argument) for a provided pattern (second argument).

test_string <- "Trader--Farmer"
## returns TRUE since the test string contains "Farmer"
str_detect(string = test_string, pattern = "Farmer")
## [1] TRUE
## returns FALSE since the test string does not contain "Student"
str_detect(test_string, "Student")
## [1] FALSE

We can use this within filter():

yao %>% 
  select(occupation) %>% 
  ## keep farmers
  filter(str_detect(occupation, "Farmer"))

From yao, keep respondents who are students.

Contributors

The following team members contributed to this lesson:

References

Some material in this lesson was adapted from the following sources:

Artwork was adapted from: